LIMO

Evaluations of JaguarCode: A web-based object-oriented programming environment with static and dynamic visualization

The mapping of agricultural land worldwide is exceptionally important since these maps provide a robust foundation in regards to the open spatial database of agriculture. Mapping processes usually and initially take part in interactive creation maps of agricultural sectors. As a result, this enables agricultural experts and specialists to undertake interactive management and monitoring of all agricultural sectors. This study was carried out in the Bukhara region of Uzbekistan. The potential and most appropriate technology of creating an interactive map of agricultural sectors of the study area was developed using ArcGIS, MapInfo, and Global Mapper software, appertain to the family of up-to-the-date GIS technologies. This software was used in the creation of agricultural spatial data and thematic layers. The created thematic layer and database were integrated into the Java applet, thus a user working window was developed and an interactive agricultural map of Bukhara province of Uzbekistan was created. The interactive map can visualize and illustrate all branches of agricultural sections as separate thematic layers and statistic diagrams.

These associated tables, references, notes, and report present a synthesis of some notable geotechnical and engineering information used to create four interactive layer maps for selected: 1) deep mines and shafts; 2) existing, considered or planned radioactive waste management deep underground studies or disposal facilities 3) deep large diameter boreholes, and 4) physics underground laboratories and facilities from around the world. These data are intended to facilitate user access to basic information and references regarding “deep underground” facilities, history, activities, and plans. In general, the interactive maps and database provide each facility’s approximate site location, geology, and engineered features (e.g.: access, geometry, depth, diameter, year of operations, groundwater, lithology, host unit name and age, basin; operator, management organization, geographic data, nearby cultural features, other). Although the survey is not comprehensive, it is representative of many of the significant existing and historical underground facilities discussed in the literature addressing radioactive waste management and deep mined geologic disposal safety systems. The global survey is intended to support and to inform: 1) interested parties and decision makers; 2) radioactive waste disposal and siting option evaluations, and 3) safety case development applicable to any mined geologic disposal facility as a demonstration of historical and current engineering and geotechnical capabilities available for use in deep underground facility siting, planning, construction, operations and monitoring.

As the most immediately impacted population, survivors’ mobility and distribution characteristics are closely linked to earthquake emergency response. Accurate population distribution and mobility data are vital foundational resources for post-disaster decision-making. With the help of mobile phone location data within the earthquake-stricken area, we explore novel rapid assessment approaches to identifying perceived impact area.The widespread adoption of smart mobile devices in China has led to the installation of numerous third-party applications. These applications rely on push notification services enabled by Push Software Development Kits (SDKs) provided by mobile service providers. These SDKs, compliant with data security standards, collection scopes, and transmission protocols, utilize built-in functional modules to periodically collect user-authorized geolocation data. This data encompasses device identifiers, GPS coordinates, WiFi signatures, cellular base station logs, and network connectivity metadata. After encryption, these multi-source data streams inputs are aggregated into structured mobile location datasets. They can provide high-resolution insights into population mobility patterns during disasters.At 9:05 (Beijing time) on 7 January 2025, an earthquake with magnitude Ms 6.8 hit Dingri County, Tibet Autonomous Region, which killed 126 people. We collected a mobile phone location dataset covering a 150 km radius from the epicenter, spanning 38 hours from 9:00 on 6 January to 1:00 on 8 January. It comprises 146,123 records. Each record includes four mobile location-based indicators, namely (1) Active Base Stations (base stations scanned and periodically reported by mobile devices), (2) Active Wi-Fi Hotspots (Wi-Fi hotspots scanned and periodically reported by mobile devices), (3) Mobile Devices (mobile devices that obtain services through multiple positioning methods) and (4) Wi-Fi-Connected Devices (mobile devices connected to Wi-Fi hotspots). Utilizing natural neighbor interpolation and Thiessen polygon interpolation methods, we analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the Dingri earthquake. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake.The research establishes a theoretical-technical framework supporting three critical post-disaster applications: (1) dynamic population distribution sensing, (2) behavioral pattern analysis of affected populations, and (3) rapid evaluation of seismic perception zones.

Abstract. The current task of cartographic support for geological exploration and the oil and gas industry is to meet the needs for rapid access to spatial data by specialists. The preparation of specialized data for publication in interactive web maps involves a number of technical issues. Some of them are explored in detail in case of a cartographic support for a cameral processing and interpretation of geological, geophysical and other geoscientific data in Andaman deep water basin. The points highlighted here are in fact a reflection of the role of interactive web maps, regardless of the thematic content, in the development of the Real and Virtual maps concept. The experimental work presented in this article illustrates most of the R/V transformations first developed by H. Moellering in early 1980s. Solving the operations for transformations between real maps, interactive maps, initial (raw) thematic data and cartographic databases will help enrich cartography with new methods of creating and using data, expanding and adapting the communicative conception of cartography to modern realities. An example of the web map developed also shows how the cartographer can simplify the work of professionals in other sectors, their communication with each other through the map language and its web representations. The use of web maps in the poorly studied Andaman deep water basin shows how web maps can contribute to the quality implementation of government programs for increasing exploration and the subsequent development of mineral resources.

The public often faces challenges in land transactions due to limited access to information. This study develops a digital land marketplace application based on Geographic Information System (GIS), named "BELITANAHKU". The primary objective of this research is to enhance the efficiency and transparency of land transactions in Indonesia by providing a platform that facilitates access to spatial information and legal ownership data. This study employs the Rapid Application Development (RAD) approach with a qualitative descriptive data analysis technique. The collected data includes Parcel Identification Number (NIB), location, land area, ownership status, and potential issues, obtained through observation and interviews.The research findings indicate that "BELITANAHKU" provides comprehensive land information, enables sellers to easily upload land data, and allows buyers to compare properties based on location and price through an interactive map. This platform is expected to address challenges in land transactions, such as legal certainty and fraud risks. With further development, this application has great potential to drive the digitalization of the land market in Indonesia and facilitate online transactions for both the public and real estate businesses. Keywords: Land Market Digitalization in Indonesia, Digital Land Marketplace, Geographic Information System (GIS), e-Conveyancing, Interactive Property Map Application. INTISARI Masyarakat sering menghadapi kendala dalam transaksi tanah akibat keterbatasan akses informasi. Penelitian ini mengembangkan aplikasi marketplace digital tanah berbasis Sistem Informasi Geografis (SIG) yang diberi nama "BELITANAHKU". Tujuan utama penelitian ini adalah meningkatkan efisiensi dan transparansi dalam transaksi jual beli tanah di Indonesia dengan menyediakan platform yang mempermudah akses informasi spasial serta data legal kepemilikan tanah. Penelitian ini menerapkan pendekatan Rapid Application Development (RAD) dengan teknik analisis data deskriptif kualitatif. Data yang dikumpulkan meliputi Nomor Identifikasi Bidang (NIB), lokasi, luas tanah, status kepemilikan, serta indikasi permasalahan, yang diperoleh melalui metode observasi dan wawancara. Hasil penelitian menunjukkan bahwa "BELITANAHKU" dapat menyediakan akses informasi tanah yang komprehensif, memungkinkan penjual untuk mengunggah data tanah dengan mudah, serta memberikan kemudahan bagi pembeli dalam membandingkan properti berdasarkan lokasi dan harga melalui peta interaktif. Platform ini diharapkan dapat mengatasi permasalahan dalam transaksi tanah, seperti kepastian hukum dan risiko penipuan. Dengan pengembangan lebih lanjut, aplikasi ini memiliki potensi besar dalam mendorong digitalisasi pasar tanah di Indonesia serta mempermudah masyarakat maupun pelaku usaha properti dalam melakukan transaksi secara online. Kata Kunci: Digitalisasi Pasar Tanah Indonesia, Marketplace Tanah Digital, Sistem Informasi Geografis (SIG), Transaksi Jual Beli Tanah Online, Aplikasi Peta Interaktif Properti.

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The development of information and communication technology has encouraged changes in maps from two-dimensional forms to interactive maps which are more efficient in conveying information. This research discusses the analysis and development of an interactive map of Jakarta State University (UNJ) using the Canva application. With four campus locations spread across Jakarta, Jakarta State University (UNJ) faces difficulties in providing location information for users. Even though they have a web-based information system, none yet displays spatial information on campus buildings. It is hoped that the development of a web-based interactive map will solve existing challenges. Through a Research and Development (R&D) research method approach, with a 4D model. Creating interactive maps using Canva involves design, template selection, adding text, and integration with website links and Google Maps. The research results show the relevance of making UNJ interactive maps in providing more complete and easily accessible information. This development is expected to increase access to information about campuses and the quality of providing geographic information in the digital era.

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.

The 2004 launch of Google Earth marked a sea change in how spatial data could be viewed and shared. This free software is an interactive, virtual globe program permitting users to actively manipulate a virtual sphere representing Earth that is overlaid with satellite or high-resolution aerial imagery of continental regions and ocean islands (Figure 1). Numerous books have also been produced on its use, for example Brown (2006) and Crowder (2007). The paper by Butler (2006) argues that online tools, led by Google Earth, are changing the way we interact with spatial data. As of May 2011 the publishers Elsevier announced that Google Maps, basically an online and more simplified version of Google Earth, functionality is now available in its journals. This new feature enriches online articles on SciVerse, ScienceDirect with interactive maps, adapting to the requirements of each scientific discipline to visualize and interact with the author’s spatial data. The maps use Google KML (geographic annotation) file format and are created by the authors.

Spatial decisions increasingly are made by both professional and citizen stakeholders using interactive maps, yet few empirically-derived guidelines exist for designing interactive maps that support complex reasoning and decision making across problem contexts. We address this gap through an online map study with 122 participants with varying expertise. The study required participants to assume two hypothetical scenarios in the North American hazardous waste trade, review geographic information on environmental justice impacts using a different interactive map for each scenario, and arrive at an optimal decision outcome. This study followed a 2 × 2 factorial design, varying interface complexity (the number of supported interaction operators) and decision complexity (the number of decision criteria) as the independent variables and controlling for participant expertise with the hazardous waste trade and other aspects of cartographic design. Our findings indicate that interface complexity, not decision complexity, influenced decision outcomes, with participants arriving at better decisions using the simpler interface. However, expertise was a moderating effect, with experts and non-experts using different interaction strategies to arrive at their decisions. The research contributes to cartography, geovisualization, spatial decision science, urban planning, and visual analytics as well as to scholarship on environmental justice, the geography of hazardous waste, and participatory mapping.

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

The article describes the work of the developed web resource "Geography of childhood" www.childgeography.com, it was implemented as part of the project of the Russian Foundation for basic research "Geography of childhood: an interdisciplinary synthesis of research approaches and practices". It also describes the main sections of the site and describes in detail how the module for managing the state of the interactive map and related controls on the information resource works. The developed module is designed to work as part of the child Geography information resource, which manages the generation of an interactive map based on geographical data from 85 regions of the Russian Federation and its accompanying statistical data for 2017. The module allows you to perform clusterization of statistical data by values, as well as visually demonstrate them for various regions of the Russian Federation.

Many of the database research issues involved in dealing with multimedia data are similar to those that arise in handling other nontraditional data such as spatial, image, temporal, text, document, and scientific. We focus here on multimedia database issues, which we hope can serve as a starting point for a wider discussion. Our examples are often taken from the spatial domain as this is where we have the greatest expertise, although these issues are far more general. Why do we want a database? The natural and simple answer is to be able to store and retrieve data efficiently. Notice the emphasis on retrieval. We should not lose sight of this purpose. For example, it means that storing images in long fields in a relational database is usually not the answer. Long fields are usually a stopgap solution as they are just a repository for data and do not aid in its retrieval. In particular, as the data volume gets large, this solution breaks down because the tuples get too large. We need to be able to integrate nontraditional data with traditional (e.g., alphanumeric) data. Alphanumeric data can frequently be treated just like locational data in that the records that make up the alphanumeric data are like points in a higher-dimensional space where each attribute is analogous to a spatial dimension. The difference is that spatial data have more than just a locational component. In particular, spatial data are distinguished from nonspatial data by having spatial extent. A number of attempts at integration take advantage of this analogy. However, it can also act as a straitjacket in the case of the relational model. Some examples of successful integration include spatial with nonspatial data [Aref and Samet 1990], temporal with nontemporal data including spatial data [Hjaltason and Samet 1995], document with nondocument data [SacksDavis et al. 1995], image with locational and nonvocational data [Samet and Soffer 1995], and so on. Efficient retrieval is facilitated by building an index [Samet 1990a, 1990b]. This means that we need to find a way to sort the data. Surprisingly, this is not always done for such applications (e.g., it is absent in the Photobook image database system [Pentland et al. 1994]). The index should be compatible with the data that are being stored, and we also need to choose an appropriate zero or reference point for it. The index should be implicit rather than explicit, as it is impossible to foresee all possible queries in advance. For example, in the spatial domain, assuming a relational model, it is impractical to have an attribute for each spatial relationship (e.g., north, northeast, left, etc.). Instead, the index should enable us to derive these relationships on the fly. As a more concrete example, an explicit index would sort twodimensional locational data on the basis of distance from a given point x; yet this would not be very useful if we wanted to have the locations sorted with respect to a different point y. In particular, we

A common approach to analyze geo-related data is using bar charts or x-y plots. They are intuitive and easy to use. But important information often gets lost. In this paper, we introduce a new interactive visualization technique called Geo Pixel Bar Charts, which combines the advantages of Pixel Bar Charts and interactive maps. This technique allows analysts to visualize large amounts of spatial data without aggregation and shows the geographical regions corresponding to the spatial data attribute at the same time. In this paper, we apply Geo Pixel Bar Charts to visually mining sales transactions and Internet usage from different locations. Our experimental results show the effectiveness of this technique for providing data distribution and immediate identification of anomalies from the map.